An efficient technique to detect skin Disease Using Image Processing

Kumar, Arhath; Shetty, Pallavi; Balipa, Mamatha; Rao, B. Raghava; Puneeth, B R; Shravya, .

doi:10.1109/aide57180.2022.10060001

Cited by 1 publication

(1 citation statement)

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“…Advancements in medical technology utilizing photonics and lasers have facilitated rapid and precise diagnosis of skin diseases. Nevertheless, the expense associated with such diagnostic methods remains a significant barrier, making them unaffordable for many individuals [7]. Consequently, we propose utilizing image processing techniques for skin disease diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Ai Empowered Diagnosis of Pemphigus: A Machine Learning Approach for Automated Skin Lesion Detection

Ahmed,

Islam,

Alif

et al. 2023

IAPGOS

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Pemphigus is a skin disease that can cause a serious damage to human skin. Pemphigus can result in other issues including painful patches and infected blisters, which can result in sepsis, weight loss, and starvation, all of which can be life-threatening, tooth decay and gum disease. Early prediction of Pemphigus may save us from fatal disease. Machine learning has the potential to offer a highly efficient approach for decision-making and precise forecasting. The healthcare sector is experiencing remarkable advancements through the utilization of machine learning techniques. Therefore, to identify Pemphigus using images, we suggested machine learning-based techniques. This proposed system uses a large dataset collected from various web sources to detect Pemphigus. Augmentation has been applied on our dataset using techniques such as zoom, flip, brightness, distortion, magnitude, height, width to enhance the breadth and variety of the dataset and improve model’s performance. Five popular machine learning algorithms has been employed to train and evaluate model, these are K-Nearest Neighbor (referred to as KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Convolutional Neural Network (CNN). Our outcome indicate that the CNN based model outperformed the other algorithms by achieving accuracy of 93% whereas LR, KNN, RF and DT achieved accuracies of 78%, 70%, 85% and 75% respectively.

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Section: Introductionmentioning

confidence: 99%